Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment

Firmbach D, Benz M, Kuritcyn P, Bruns V, Lang-Schwarz C, Stübs F, Merkel S, Leikauf LS, Braunschweig AL, Oldenburger A, Gloßner L, Abele N, Eck C, Matek C, Hartmann A, Geppert CI (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Cancers MDPI

Book Volume: 15

Article Number: 2675

Journal Issue: 10

DOI: 10.3390/cancers15102675

Abstract

The tumor–stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor–stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor–stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.

Authors with CRIS profile

Frederik Stübs Department of Obstetrics and Gynaecology Susanne Merkel Department of Surgery Leah-Sophie Leikauf Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Christine Eck Department of Nephropathology Christian Matek Institute of Pathology Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Carol-Immanuel Geppert Institute of Pathology

Additional Organisation(s)

Interdisziplinäres Zentrum für Klinische Forschung

Related research project(s)

J101: AI-based support for histopathology evaluation of autoinflammatory and malignant GI diseases Jan. 1, 2023 - June 30, 2025

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE) Klinikum Bayreuth DE Germany (DE)

How to cite

APA:

Firmbach, D., Benz, M., Kuritcyn, P., Bruns, V., Lang-Schwarz, C., Stübs, F.,... Geppert, C.-I. (2023). Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment. Cancers, 15(10). https://doi.org/10.3390/cancers15102675

MLA:

Firmbach, Daniel, et al. "Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment." Cancers 15.10 (2023).

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